Description: 机器学习文本分类的SVM算法实现，VC++ 6.0环境下编译-A SVM algorithm for text classification in machine learning, and compiled under the Visual C++ 6.0 environment. Platform: |
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Author:邵云 |
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Description: Support Vector Machines is a powerful methodology for solving problems in nonlinear classification and regression.
It is a matlab version.-Support Vector Machines is a powerful methamphetamine odology for solving problems in nonlinear CLAS sification and regression. It is a Matlab versi on. Platform: |
Size: 29696 |
Author:Mountain |
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Description: svm在matlab环境下多类分类中的一对多分类器类型程序-svm in Matlab environment over classification of the one-to-many types of procedures classifier Platform: |
Size: 2048 |
Author:李李 |
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Description: libsvm is a simple, easy-to-use, and efficient software for SVM
classification and regression. It solves C-SVM classification, nu-SVM
classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
regression. It also provides an automatic model selection tool for
C-SVM classification. This document explains the use of libsvm.
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Author:baolij |
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Description: 在做2維度樣本分類的過程中,若我們能事先畫出訓練樣本在空間中的分散情形,這將有助於我們在設定SVM分類器的參數C的取值範圍. 例如:若畫出的訓練樣本的散佈較分散,我們可以得知此時採用的參數值可以取在較大的範圍. 所以本程式也是讓想要畫出資料樣本在平面的散佈情形者之一各可行工具.-2 dimensions in a sample to do the classification process, if we were able to pre-draw training samples dispersed in space situation, this will help us in setting the parameters of SVM classifier C range. For example: If the draw training samples to spread more decentralized, we can know at this time using the parameter values can be chosen in a larger scope. So let this program is also want to draw information on the spread of samples in the planar case, one of the possible tools. Platform: |
Size: 1024 |
Author:worthwhile |
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Description: 支持向量机一个很好的，较新的一个进行分析分析的软件-Recent advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely.Support Vector Machine is useful software Platform: |
Size: 27648 |
Author:chenyq |
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Description: this a matlab coding of svm classification function.
when inputing training samples, training labels, testing samples, testing labels, and two parameters, the classification result is obtained.
linear svm and nonlinear svm can be selected.-this is a matlab coding of svm classification function. when inputing training samples, training labels, testing samples, testing labels, and two parameters, the classification result is obtained. linear svm and nonlinear svm can be selected. Platform: |
Size: 1024 |
Author:Phoenix |
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Description: In this code I have used GA in supervised PCA to find the best coeficients for overall covariance. the classification is made by K-In this code I have used GA in supervised PCA to find the best coeficients for overall covariance. the classification is made by KNN Platform: |
Size: 20480 |
Author:fatal |
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Description: SVM方法的基本思想是：定义最优线性超平面，并把寻找最优线性超平面的算法归结为求解一个凸规划问题。进而基于Mercer核展开定理，通过非线性映射φ，把样本空间映射到一个高维乃至于无穷维的特征空间（Hilbert空间），使在特征空间中可以应用线性学习机的方法解决样本空间中的高度非线性分类和回归等问题。svm 程序，即支持向量机的代码。-The basic idea of SVM method are: the definition of the optimal linear hyperplane, and the search algorithm for optimal linear hyperplane by solving a convex programming problem. Then based on Mercer nuclear expansion theorem, through a nonlinear mapping φ, the sample space is mapped to a high-dimensional and even infinite dimensional feature space (Hilbert space), so that in the feature space can be applied to solve the linear learning machine method, the sample space The highly nonlinear classification and regression problems. svm procedures that support vector machine code. Platform: |
Size: 117760 |
Author:秀 |
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Description: SVM神经网络的数据分类预测在葡萄酒种类识别中的应用-SVM classification neural network prediction of data types in the identification of wine Platform: |
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Author:田震 |
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Description: 支持向量机的研究现已成为机器学习领域中的研究热点，其理论基础是Vapnik[3]等提出的统计学习理论。统计学习理论采用结构风险最小化准则，在最小化样本点误差的同时，缩小模型泛化误差的上界，即最小化模型的结构风险，从而提高了模型的泛化能力，这一优点在小样本学习中更为突出。SVM理论正是在这一基础上发展而来的，经过十几年的研究和发展，已开始逐步应用于一些领域。在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，已经在模式识别、函数逼近和概率密度估计等方面取得了良好的效果。- Support Vector Machine (SVM) is a new machine learning technique in recent years developed based on statistical learning theory (SLT). It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. The theory and algorithm of SVC is studied at first, then, simulation is to recognize handwritten numeral with the Lib-SVM toolbox. At last, we study the result, which shows that the SVC can do the classification problem with good performance, shorter operation time and is more suitable for real-time implementation. Platform: |
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Author:任修齐 |
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Description: 在matlab环境下实现的算法，该算法主要实现SVM分类的算法，通过SVM实现文字识别-In the matlab environment to achieve algorithm to achieve SVM classification of the main algorithm, character recognition by SVM to achieve Platform: |
Size: 1024 |
Author:王娟 |
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Description: 在matlab环境下实现的算法，该算法主要实现SVM分类的算法，svm入门-In the matlab environment to achieve algorithm to achieve SVM classification of the main algorithm, character recognition by SVM to achieve Platform: |
Size: 254976 |
Author:王娟 |
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Description: 图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。
其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？
用BoW描述完图像之后，指的是将训练集以及测试集的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。
在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。-Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set.
In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem?
After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified.
Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram Platform: |
Size: 3585024 |
Author:lipiji |
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Description: svm插件，安装在MATBLA上面，用于学习SVM分类，对于SVM分类有很大帮助！(SVM plug-in, installed on the MATBLA, is used to learn SVM classification, which is helpful for SVM classification.) Platform: |
Size: 230400 |
Author:猪猪1234
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